RESUMEN

PURPOSE: Vision Transformers recently achieved a competitive performance compared with CNNs due to their excellent capability of learning global representation. However, there are two major challenges when applying them to 3D image segmentation: i) Because of the large size of 3D medical images, comprehensive global information is hard to capture due to the enormous computational costs. ii) Insufficient local inductive bias in Transformers affects the ability to segment detailed features such as ambiguous and subtly defined boundaries. Hence, to apply the Vision Transformer mechanism in the medical image segmentation field, the above challenges need to be overcome adequately. METHODS: We propose a hybrid paradigm, called Variable-Shape Mixed Transformer (VSmTrans), that integrates self-attention and convolution and can enjoy the benefits of free learning of both complex relationships from the self-attention mechanism and the local prior knowledge from convolution. Specifically, we designed a Variable-Shape self-attention mechanism, which can rapidly expand the receptive field without extra computing cost and achieve a good trade-off between global awareness and local details. In addition, the parallel convolution paradigm introduces strong local inductive bias to facilitate the ability to excavate details. Meanwhile, a pair of learnable parameters can automatically adjust the importance of the above two paradigms. Extensive experiments were conducted on two public medical image datasets with different modalities: the AMOS CT dataset and the BraTS2021 MRI dataset. RESULTS: Our method achieves the best average Dice scores of 88.3 % and 89.7 % on these datasets, which are superior to the previous state-of-the-art Swin Transformer-based and CNN-based architectures. A series of ablation experiments were also conducted to verify the efficiency of the proposed hybrid mechanism and the components and explore the effectiveness of those key parameters in VSmTrans. CONCLUSIONS: The proposed hybrid Transformer-based backbone network for 3D medical image segmentation can tightly integrate self-attention and convolution to exploit the advantages of these two paradigms. The experimental results demonstrate our method's superiority compared to other state-of-the-art methods. The hybrid paradigm seems to be most appropriate to the medical image segmentation field. The ablation experiments also demonstrate that the proposed hybrid mechanism can effectively balance large receptive fields with local inductive biases, resulting in highly accurate segmentation results, especially in capturing details. Our code is available at https://github.com/qingze-bai/VSmTrans.

Asunto(s)

RESUMEN

Background: The impact of early glenohumeral osteoarthritis (GHOA) on clinical outcomes after rotator cuff repair (RCR) remains unclear. The magnetic resonance imaging (MRI)-based Shoulder Osteoarthritis Severity (SOAS) score is a comprehensive approach to quantifying glenohumeral degeneration. Purpose: To investigate the association between SOAS scores and changes in American Shoulder and Elbow Surgeons (ASES) scores in patients who underwent RCR. Study Design: Cohort study; Level of evidence, 3. Methods: Two reviewers independently analyzed the preoperative MRI scans of 116 shoulders and assigned SOAS scores. Spearman correlation was used to calculate the association of mean SOAS scores with patient demographic characteristics and change in ASES scores over the 2-year follow-up period (ΔASES). Multivariate regression analysis was performed between the independent variables of patient age, sex, body mass index, and significant SOAS score components as determined by univariate analysis, with the dependent variable being ΔASES. Significance was defined as P < .05 for univariate analysis and P < .0125 after application of the Bonferroni correction for multivariate analysis. Results: The mean ASES scores were 55.8 ± 18.6 preoperatively and 92.1 ± 12.1 at 2 years postoperatively. The mean preoperative SOAS score was 15.2 ± 7.1. On univariate analysis, the total SOAS score was positively correlated with patient age (r S = 0.41; P < .001), whereas ΔASES was negatively correlated with patient age (r S = -0.27; P = .0032). Increasing SOAS subscores for supraspinatus/infraspinatus tear size (r S = -0.28; P = .024), tendon retraction (r S = -0.23; P = .015), muscle atrophy (r S = -0.20; P = .034), paralabral ganglia (r S = -0.23; P = .015), and cartilage degeneration (r S = -0.21; P = .024) were negatively correlated with ΔASES. A negative correlation was found between increasing total SOAS score and ΔASES (r S = -0.22; P = .016). On multivariate analysis, increasing supraspinatus/infraspinatus tear size was significantly and negatively correlated with ΔASES (ß = -3.3; P = .010). Conclusion: Increasing the total SOAS score was predictive of less improvement in ASES scores at 2 years postoperatively. On univariate analysis, SOAS subscores with the strongest negative correlations with ΔASES scores included tear size, muscle atrophy, tendon retraction, paralabral ganglia, and cartilage wear. On multivariate analysis, only tear size was significantly associated with a lower change in the ASES score.

RESUMEN

Background: Repair of posterior medial meniscus root (PMMR) tears has demonstrated favorable outcomes and may prevent rapid progression of knee osteoarthritis; however, there is a paucity of data regarding prognostic factors affecting postoperative outcomes. Purpose/Hypothesis: The purpose of this study was to identify factors on preoperative magnetic resonance imaging (MRI) that predict postoperative outcomes after PMMR repair. It was hypothesized that patients with increasing levels of degenerative changes as evaluated through semiquantitative preoperative MRI scans would have worse postoperative patient-reported outcome (PRO) scores. Study Design: Cohort study; Level of evidence, 3. Methods: Patients who underwent PMMR repair between 2012 and 2020 and had minimum 2-year follow-up data were enrolled. Pre- and postoperative visual analog scale pain scores and postoperative PRO surveys including the Patient-Reported Outcomes Measurement Information System-Physical Function, Lysholm knee score, and Knee injury and Osteoarthritis Outcome Score (KOOS) were collected. Patients who achieved the Patient Acceptable Symptom State (PASS) on the KOOS subscales were reported. Two fellowship-trained musculoskeletal radiologists reviewed preoperative MRIs and calculated the Whole-Organ Magnetic Resonance Imaging Score for meniscus, cartilage, bone marrow edema-like lesions (BMELL), and meniscal extrusion. Statistical analysis was performed using the 2-sample t test, Mann-Whitney test, and Fisher exact test for categorical variables. Results: A total of 29 knees in 29 patients were evaluated (22 female, 7 male; mean age at surgery, 52.3 ± 9.9 years; body mass index, 27.6 ± 5.6 kg/m2; mean follow-up, 59.6 ± 26.5 months). Visual analog scale for pain scores decreased significantly from preoperatively (4.9 ± 2.0) to final follow-up (1.6 ± 1.9) (P < .001), and the percentage of patients meeting the PASS ranged from 44.8% for KOOS Sport and Recreation to 72.4% for KOOS Pain and KOOS Quality of Life. Patients with medial tibial BMELL (MT-BMELL) had significantly lower KOOS Symptoms scores (76.1 ± 17.3 vs 88.4 ± 9.7 without MT-BMELL; P = .032). Cartilage quality and presence of meniscal extrusion were not associated with outcomes. Conclusion: Patients with MT-BMELL on their preoperative MRI in the setting of PMMR tear were found to have worse KOOS Symptoms scores after PMMR repair.

RESUMEN

This paper presents a 16×20 CMOS biosensor array based on electrochemical impedance spectroscopy (EIS), a highly sensitive label-free technique for rapid disease detection at point-of-care. This high-density system implements a polar-mode detection with phase-only EIS measurement over a 5 kHz - 1 MHz frequency range. The design features predominantly digital readout circuitry, ensuring scalability with technology, along with a load-compensated transimpedance amplifier at the front, all within a 140×140 µm2; pixel. The architecture enables in-pixel digitization and accumulation, which increases the SNR by 10 dB for each 10× increase in readout time. Implemented in a 180 nm CMOS process, the 3×4 mm2 chip achieves state-of-the-art performance with an rms phase error of 0.035% at 100 kHz through a duty-cycle insensitive phase detector and one of the smallest per pixel areas with in-pixel quantization.

RESUMEN

Background: Increases in GPNMB are detectable in FTD- GRN cerebrospinal fluid (CSF) and post-mortem brain, and brains of aged Grn -deficient mice. Although no upregulation of GPNMB is observed in the brains of young Grn -deficient mice, peripheral immune cells of these mice do exhibit this increase in GPNMB. Importantly, the functional significance of GPNMB upregulation in progranulin-deficient states is currently unknown. Given that GPNMB has been discussed as a potential therapeutic target in GRN -mediated neurodegeneration, it is vital for the field to determine what the normal function of GPNMB is in the immune system, and whether targeting GPNMB will elicit beneficial or deleterious effects. Methods: The effects of GPNMB knock-down via antisense oligonucleotide (ASO) were assessed in peripheral blood mononuclear cells (PBMCs) from 25 neurologically healthy controls (NHCs) and age- and sex-matched FTD- GRN patients, as well as peritoneal macrophages (pMacs) from progranulin-deficient ( Grn -/- ) and B6 mice. Lysosomal function, antigen presentation and MHC-II processing and recycling were assessed, as well as cytokine release and transcription. Results: We demonstrate here that ASO-mediated knockdown of GPNMB increases lysosomal burden and cytokine secretion in FTD-GRN carrier and neurologically healthy controls (NHCs) monocytes. ASO-mediated knockdown of GPNMB in Grn -deficient macrophages decreased lysosomal pan-cathepsin activity and protein degradation. In addition, ASO-mediated knockdown of GPNMB increased MHC-II surface expression, which was driven by decreased MHC-II uptake and recycling, in macrophages from Grn -deficient females. Finally, ASO-mediated knockdown of GPNMB dysregulated IFNγ-stimulated cytokine transcription and secretion by mouse macrophages due to the absence of regulatory actions of the GPNMB extracellular fragment (ECF). Conclusions: Our data herein reveals that GPNMB has a regulatory effect on multiple immune effector functions, including capping inflammation and immune responses in myeloid cells via secretion of its ECF. Therefore, in progranulin-deficient states, the drastic upregulation in GPNMB transcript and protein may represent a compensatory mechanism to preserve lysosomal function in myeloid cells. These novel findings indicate that targeted depletion in FTD- GRN would not be a rational therapeutic strategy because it is likely to dysregulate important immune cell effector functions.

RESUMEN

BACKGROUND: Identification of pulmonary embolism (PE) across a cohort currently requires burdensome manual review. Previous approaches to automate capture of PE diagnosis have either been too complex for widespread use or have lacked external validation. We sought to develop and validate the Regular Expression Aided Determination of PE (READ-PE) algorithm, which uses a portable text-matching approach to identify PE in reports from computed tomography with angiography (CTA). METHODS: We identified derivation and validation cohorts of final radiology reports for CTAs obtained on adults (≥ 18 years) at two independent, quaternary academic emergency departments (EDs) in the United States. All reports were in the English language. We manually reviewed CTA reports for PE as a reference standard. In the derivation cohort, we developed the READ-PE algorithm by iteratively combining regular expressions to identify PE. We validated the READ-PE algorithm in an independent cohort, and compared performance against three prior algorithms with sensitivity, specificity, positive-predictive-value (PPV), negative-predictive-value (NPV), and the F1 score. RESULTS: Among 2948 CTAs in the derivation cohort 10.8 % had PE and the READ-PE algorithm reached 93 % sensitivity, 99 % specificity, 94 % PPV, 99 % NPV, and 0.93 F1 score, compared to F1 scores ranging from 0.50 to 0.85 for three prior algorithms. Among 1206 CTAs in the validation cohort 9.2 % had PE and the algorithm had 98 % sensitivity, 98 % specificity, 85 % PPV, 100 % NPV, and 0.91 F1 score. CONCLUSIONS: The externally validated READ-PE algorithm identifies PE in English-language reports from CTAs obtained in the ED with high accuracy. This algorithm may be used in the electronic health record to accurately identify PE for research or surveillance. If implemented at other EDs, it should first undergo local validation and may require maintenance over time.

Asunto(s)

RESUMEN

Auto-segmentation is one of the critical and foundational steps for medical image analysis. The quality of auto-segmentation techniques influences the efficiency of precision radiology and radiation oncology since high- quality auto-segmentations usually require limited manual correction. Segmentation metrics are necessary and important to evaluate auto-segmentation results and guide the development of auto-segmentation techniques. Currently widely applied segmentation metrics usually compare the auto-segmentation with the ground truth in terms of the overlapping area (e.g., Dice Coefficient (DC)) or the distance between boundaries (e.g., Hausdorff Distance (HD)). However, these metrics may not well indicate the manual mending effort required when observing the auto-segmentation results in clinical practice. In this article, we study different segmentation metrics to explore the appropriate way of evaluating auto-segmentations with clinical demands. The mending time for correcting auto-segmentations by experts is recorded to indicate the required mending effort. Five well-defined metrics, the overlapping area-based metric DC, the segmentation boundary distance-based metric HD, the segmentation boundary length-based metrics surface DC (surDC) and added path length (APL), and a newly proposed hybrid metric Mendability Index (MI) are discussed in the correlation analysis experiment and regression experiment. In addition to these explicitly defined metrics, we also preliminarily explore the feasibility of using deep learning models to predict the mending effort, which takes segmentation masks and the original images as the input. Experiments are conducted using datasets of 7 objects from three different institutions, which contain the original computed tomography (CT) images, the ground truth segmentations, the auto-segmentations, the corrected segmentations, and the recorded mending time. According to the correlation analysis and regression experiments for the five well-defined metrics, the variety of MI shows the best performance to indicate the mending effort for sparse objects, while the variety of HD works best when assessing the mending effort for non-sparse objects. Moreover, the deep learning models could well predict efforts required to mend auto-segmentations, even without the need of ground truth segmentations, demonstrating the potential of a novel and easy way to evaluate and boost auto-segmentation techniques.

RESUMEN

Pyrimidine nucleotide biosynthesis is a druggable metabolic dependency of cancer cells, and chemotherapy agents targeting pyrimidine metabolism are the backbone of treatment for many cancers. Dihydroorotate dehydrogenase (DHODH) is an essential enzyme in the de novo pyrimidine biosynthesis pathway that can be targeted by clinically approved inhibitors. However, despite robust preclinical anticancer efficacy, DHODH inhibitors have shown limited single-agent activity in phase 1 and 2 clinical trials. Therefore, novel combination therapy strategies are necessary to realize the potential of these drugs. To search for therapeutic vulnerabilities induced by DHODH inhibition, we examined gene expression changes in cancer cells treated with the potent and selective DHODH inhibitor brequinar (BQ). This revealed that BQ treatment causes upregulation of antigen presentation pathway genes and cell surface MHC class I expression. Mechanistic studies showed that this effect is (1) strictly dependent on pyrimidine nucleotide depletion, (2) independent of canonical antigen presentation pathway transcriptional regulators, and (3) mediated by RNA polymerase II elongation control by positive transcription elongation factor B (P-TEFb). Furthermore, BQ showed impressive single-agent efficacy in the immunocompetent B16F10 melanoma model, and combination treatment with BQ and dual immune checkpoint blockade (anti-CTLA-4 plus anti-PD-1) significantly prolonged mouse survival compared to either therapy alone. Our results have important implications for the clinical development of DHODH inhibitors and provide a rationale for combination therapy with BQ and immune checkpoint blockade.

Asunto(s)

Asunto(s)

RESUMEN

Background: Given the complexity of arthroscopic rotator cuff repair (ARCR) and increasing prevalence, there is a need for comprehensive, large-scale studies that investigate potential correlations between surgeon-specific factors and postoperative outcomes after ARCR. This study examines how surgeon-specific factors including case volume, career length, fellowship training, practice setting, and regional practice impact two-year reoperation rates, conversion to total shoulder arthroplasty (anatomic or reverse), and 90-day post-ARCR hospitalization. Methods: The PearlDiver Mariner database was used to collect surgeon-specific variables and query patients who underwent ARCR from 2015 to 2018. Patient outcomes were tracked for two years, including reoperations, hospitalizations, and International Classification of Diseases, Tenth Revision codes for revision rotator cuff repair (RCR) laterality. Hospitalizations were defined as any emergency department (ED) visit or hospital readmission within 90 days after primary ARCR. Surgeon-specific factors including surgeon case volume, career length, fellowship training, practice setting, and regional practice were analyzed in relation to postoperative outcomes using both univariate and multivariate logistic regression. Results: 94,150 patients underwent ARCR by 1489 surgeons. On multivariate analysis, high-volume surgeons demonstrated a higher risk for two-year total reoperation (odds ratio [OR] = 1.06, 95% confidence interval [CI]: 1.01-1.12, P = .03) and revision RCR (OR = 1.06, 95% CI: 1.01-1.12, P = .02) compared to low-volume surgeons. Early-career surgeons showed higher rates of 90-day ED visits (mid-career surgeons: OR = 0.78, 95% CI: 0.73-0.83, P < .001; late-career surgeons: OR = 0.73, 95% CI: 0.68-0.78, P < .001) and hospital readmission (mid-career surgeons: OR = 0.74, 95% CI: 0.63-0.87, P < .001; late-career surgeons: OR = 0.73, 95% CI: 0.61-0.88, P = .006) compared to mid- and late-career surgeons. Sports medicine and/or shoulder and elbow fellowship-trained surgeons demonstrated lower two-year reoperation risk (OR = 0.95, CI: 0.91-0.99, P = .04) and fewer 90-day ED visits (OR = 0.93, 95% CI = 0.88-0.98, P = .002). Academic surgeons experienced higher readmission rates compared to community surgeons (OR = 1.16, 95% CI = 1.01-1.34, P = .03). Surgeons practicing in the Northeast demonstrated lower two-year reoperation (OR = 0.88, 95% CI: 0.83-0.93, P < .001) and revision (OR = 0.88, 95% CI: 0.83-0.94, P < .001) RCR risk compared to surgeons in the Southern United States. Conclusion: High-volume surgeons exhibit higher two-year reoperation rates after ARCR compared to low-volume surgeons. Early-career surgeons demonstrate increased hospitalizations. Sports medicine or shoulder and elbow surgery fellowships correlate with reduced two-year reoperation rates and 90-day ED visits.

RESUMEN

Background: Published guidelines that help clinicians identify patients who would benefit from the co-prescription of intranasal naloxone (IN) exclude "palliative care patients." In the absence of clear care standards, palliative care (PC) clinicians may experience uncertainty in how to approach IN co-prescriptions. Objective: Explore the attitudes of PC clinicians in the United States of America who work at regional health care institutions regarding IN prescriptions for patients they prescribe opioids for. Methods: An 18-question electronic survey was distributed to PC clinicians that practice at institutions in Wisconsin or Minnesota with at least 10 other PC clinicians between February and May 2023. The survey explored clinical scenarios in which respondents would and would not prescribe IN. Results: Fifty-six PC clinicians responded to the survey-response rate 41%. Most respondents (90.9%) did not feel IN prescriptions should be reserved for patients with a full code status; 67.9% of respondents felt that IN prescriptions are reasonable for certain patients with a terminal illness and comfort goals of care. Neither prognosis, duration of opioid therapy, nor dose of opioid therapy were significant factors in determining whether most respondents prescribed IN for their patients. Most respondents (81.8%) felt clinician counseling and patient consent were essential before prescribing IN. Conclusion: Most PC clinicians in our survey felt that IN prescriptions can be appropriate for patients they prescribe opioids for. Bystander safety was an emerging rationale for why respondents chose to prescribe IN for their patients. Despite public health efforts to make IN more freely available, most respondents felt clinician counseling was essential before prescribing IN for their patients.

RESUMEN

Organ segmentation is a fundamental requirement in medical image analysis. Many methods have been proposed over the past 6 decades for segmentation. A unique feature of medical images is the anatomical information hidden within the image itself. To bring natural intelligence (NI) in the form of anatomical information accumulated over centuries into deep learning (DL) AI methods effectively, we have recently introduced the idea of hybrid intelligence (HI) that combines NI and AI and a system based on HI to perform medical image segmentation. This HI system has shown remarkable robustness to image artifacts, pathology, deformations, etc. in segmenting organs in the Thorax body region in a multicenter clinical study. The HI system utilizes an anatomy modeling strategy to encode NI and to identify a rough container region in the shape of each object via a non-DL-based approach so that DL training and execution are applied only to the fuzzy container region. In this paper, we introduce several advances related to modeling of the NI component so that it becomes substantially more efficient computationally, and at the same time, is well integrated with the DL portion (AI component) of the system. We demonstrate a 9-40 fold computational improvement in the auto-segmentation task for radiation therapy (RT) planning via clinical studies obtained from 4 different RT centers, while retaining state-of-the-art accuracy of the previous system in segmenting 11 objects in the Thorax body region.

RESUMEN

Medical image auto-segmentation techniques are basic and critical for numerous image-based analysis applications that play an important role in developing advanced and personalized medicine. Compared with manual segmentations, auto-segmentations are expected to contribute to a more efficient clinical routine and workflow by requiring fewer human interventions or revisions to auto-segmentations. However, current auto-segmentation methods are usually developed with the help of some popular segmentation metrics that do not directly consider human correction behavior. Dice Coefficient (DC) focuses on the truly-segmented areas, while Hausdorff Distance (HD) only measures the maximal distance between the auto-segmentation boundary with the ground truth boundary. Boundary length-based metrics such as surface DC (surDC) and Added Path Length (APL) try to distinguish truly-predicted boundary pixels and wrong ones. It is uncertain if these metrics can reliably indicate the required manual mending effort for application in segmentation research. Therefore, in this paper, the potential use of the above four metrics, as well as a novel metric called Mendability Index (MI), to predict the human correction effort is studied with linear and support vector regression models. 265 3D computed tomography (CT) samples for 3 objects of interest from 3 institutions with corresponding auto-segmentations and ground truth segmentations are utilized to train and test the prediction models. The five-fold cross-validation experiments demonstrate that meaningful human effort prediction can be achieved using segmentation metrics with varying prediction errors for different objects. The improved variant of MI, called MIhd, generally shows the best prediction performance, suggesting its potential to indicate reliably the clinical value of auto-segmentations.

RESUMEN

Cyclin-dependent kinase 9 (CDK9) coordinates signaling events that regulate RNA polymerase II (Pol II) pause-release states. It is an important co-factor for transcription factors, such as MYC, that drive aberrant cell proliferation when their expression is deregulated. CDK9 modulation offers an approach for attenuating dysregulation in such transcriptional programs. As a result, numerous drug development campaigns to inhibit CDK9 kinase activity have been pursued. More recently, targeted degradation has emerged as an attractive approach. However, comprehensive evaluation of degradation versus inhibition is still critically needed to assess the biological contexts in which degradation might offer superior therapeutic benefits. We validated that CDK9 inhibition triggers a compensatory mechanism that dampens its effect on MYC expression and found that this feedback mechanism was absent when the kinase is degraded. Importantly, CDK9 degradation is more effective than its inhibition for disrupting MYC transcriptional regulatory circuitry likely through the abrogation of both enzymatic and scaffolding functions of CDK9. Highlights: - KI-CDK9d-32 is a highly potent and selective CDK9 degrader. - KI-CDK9d-32 leads to rapid downregulation of MYC protein and mRNA transcripts levels. - KI-CDK9d-32 represses canonical MYC pathways and leads to a destabilization of nucleolar homeostasis. - Multidrug resistance ABCB1 gene emerged as the strongest resistance marker for the CDK9 PROTAC degrader.

RESUMEN

Purpose To investigate free-breathing thoracic bright-blood four-dimensional (4D) dynamic MRI (dMRI) to characterize aeration of parenchymal lung tissue in healthy children and patients with thoracic insufficiency syndrome (TIS). Materials and Methods All dMR images in patients with TIS were collected from July 2009 to June 2017. Standardized signal intensity (sSI) was investigated, first using a lung aeration phantom to establish feasibility and sensitivity and then in a retrospective research study of 40 healthy children (16 male, 24 female; mean age, 9.6 years ± 2.1 [SD]), 20 patients with TIS before and after surgery (11 male, nine female; mean age, 6.2 years ± 4.2), and another 10 healthy children who underwent repeated dMRI examinations (seven male, three female; mean age, 9 years ± 3.6). Individual lungs in 4D dMR images were segmented, and sSI was assessed for each lung at end expiration (EE), at end inspiration (EI), preoperatively, postoperatively, in comparison to normal lungs, and in repeated scans. Results Air content changes of approximately 6% were detectable in phantoms via sSI. sSI within phantoms significantly correlated with air occupation (Pearson correlation coefficient = -0.96 [P < .001]). For healthy children, right lung sSI was significantly lower than that of left lung sSI (at EE: 41 ± 6 vs 47 ± 6 and at EI: 39 ± 6 vs 43 ± 7, respectively; P < .001), lung sSI at EI was significantly lower than that at EE (P < .001), and left lung sSI at EE linearly decreased with age (r = -0.82). Lung sSI at EE and EI decreased after surgery for patients (although not statistically significantly, with P values of sSI before surgery vs sSI after surgery, left and right lung separately, in the range of 0.13-0.51). sSI varied within 1.6%-4.7% between repeated scans. Conclusion This study demonstrates the feasibility of detecting change in sSI in phantoms via bright-blood dMRI when air occupancy changes. The observed reduction in average lung sSI after surgery in pediatric patients with TIS may indicate postoperative improvement in parenchymal aeration. Keywords: MR Imaging, Thorax, Lung, Pediatrics, Thoracic Surgery, Lung Parenchymal Aeration, Free-breathing Dynamic MRI, MRI Intensity Standardization, Thoracic Insufficiency Syndrome Supplemental material is available for this article. © RSNA, 2024.

Asunto(s)

RESUMEN

BACKGROUND: The International Prostate Symptom Score (IPSS) is a patient-reported measurement to assess the lower urinary tract symptoms of bladder outlet obstruction. Bladder outlet obstruction induces molecular and morphological alterations in the urothelium, suburothelium, detrusor smooth muscle cells, detrusor extracellular matrix, and nerves. We sought to analyze MRI-based radiomics features of the urinary bladder wall and their association with IPSS. METHOD: In this retrospective study, 87 patients who had pelvic MRI scans were identified. A biomarker discovery approach based on the optimal biomarker (OBM) method was used to extract features of the bladder wall from MR images, including morphological, intensity-based, and texture-based features, along with clinical variables. Mathematical models were created using subsets of features and evaluated based on their ability to discriminate between low and moderate-to-severe IPSS (less than 8 vs. equal to or greater than 8). RESULTS: Of the 7,666 features per patient, four highest-ranking optimal features were derived (all texture-based features), which provided a classification accuracy of 0.80 with a sensitivity, specificity, and area under the receiver operating characteristic curve of 0.81, 0.81, and 0.87, respectively. CONCLUSION: A highly independent set of urinary bladder wall features derived from MRI scans were able to discriminate between patients with low vs. moderate-to-severe IPSS with accuracy of 80%. Such differences in MRI-based properties of the bladder wall in patients with varying IPSS's might reflect differences in underlying molecular and morphological alterations that occur in the setting of chronic bladder outlet obstruction.

Asunto(s)

RESUMEN

PURPOSE: To compare rates of revisions between patients with isolated anterior cruciate ligament (ACL) reconstruction and those who had concomitant medial collateral ligament (MCL) injuries managed either operatively or nonoperatively at the time of index anterior cruciate ligament reconstruction (ACLR). METHODS: Using laterality-specific International Classification of Diseases, Tenth Revision (ICD-10) and Current Procedural Terminology (CPT) codes, we queried the PearlDiver-Mariner Database for all patients who underwent ACLR between 2016 and 2020. Patients were included if they were ages 15 or older and had a minimum of 2 years of follow-up after index ACLR. Patients were then divided into cohorts by presence or absence of concomitant MCL injury. The cohort of concomitant MCL injuries was further subdivided into those with MCL injuries managed nonoperatively, with MCL repair, or with MCL reconstruction at the time of index ACLR. Multivariate regression was performed between cohorts to evaluate for factors associated with revision ACLR. RESULTS: We identified 47,306 patients with isolated ACL injuries and 10,846 with concomitant MCL and ACL injuries. In total, 93% of patients with concomitant MCL injuries had their MCL treated nonoperatively; however, the annual proportion of patients being surgically managed for their MCL injury increased by 70% from 2016 to 2020. Concomitant MCL injury patients had greater odds of undergoing revision ACLR compared with patients with isolated ACL injuries (odds ratio 1.50, 95% confidence interval 1.36-1.66, P < .001). Among patients with concomitant MCL injuries, surgically managed patients had a greater risk of revision ACLR compared with nonoperatively managed MCL injuries (odds ratio 1.39, 95% confidence interval 1.01-1.86, P = .034). CONCLUSIONS: Despite an increase in operatively managed concomitant MCL injuries, most concomitant MCL injuries were still managed nonoperatively at the time of ACLR. Patients with concomitant MCL injuries, particularly those managed operatively, at the time of ACLR are at increased risk of requiring revision ACLR compared with those with isolated ACL injuries. LEVEL OF EVIDENCE: Level III, retrospective comparative case series.